LCC's Pinpoint temporal and spatial normalization system unlocks value from raw texts by automatically converting temporal and spatial expressions into formats that can be easily visualized.

While state-of-the-art named entity recognition systems (like LCC's CiceroLite) can identify names of locations, dates, and times with near-human accuracy, natural language texts are full of underspecified or inexact temporal or spatial expressions that must be resolved before they can be utilized by an information consumer.

Pinpoint leverages an award-winning combination of machine-learning and heuristic-based approaches to associate instances of dates, times, and locations in texts with a normalized expression that can be exported to a wide range of visualization or natural language processing applications.

Whether used in conjunction with entity annotations provided by LCC's CiceroLite - or other commerically-available entity recognition solutions - Pinpoint provides the accurate and reliable temporal and spatial expression resolution information consumers need to take full advantage of text annotation tools.

Designed for easy deployment, Pinpoint is released with a pure Java API that allows for straightforward integration with other information processing systems. A wide range of file formats are supported, including HTML, SGML, XML, PDF, and Microsoft Word documents.